[2506.20370] InvZW: Invariant Feature Learning via Noise-Adversarial Training for Robust Image Zero-Watermarking

Computer Science > Computer Vision and Pattern Recognition arXiv:2506.20370 (cs) [Submitted on 25 Jun 2025 (v1), last revised 1 Apr 2026 (this version, v2)] Title:InvZW: Invariant Feature Learning via Noise-Adversarial Training for Robust Image Zero-Watermarking Authors:Abdullah All Tanvir, Frank Y. Shih, Xin Zhong View a PDF of the paper titled InvZW: Invariant Feature Learning via Noise-Adversarial Training for Robust Image Zero-Watermarking, by Abdullah All Tanvir and 2 other authors View PDF HTML (experimental) Abstract:This paper introduces a novel deep learning framework for robust image zero-watermarking based on distortion-invariant feature learning. As a zero-watermarking scheme, our method leaves the original image unaltered and learns a reference signature through optimization in the feature space. The proposed framework consists of two key modules. In the first module, a feature extractor is trained via noise-adversarial learning to generate representations that are both invariant to distortions and semantically expressive. This is achieved by combining adversarial supervision against a distortion discriminator and a reconstruction constraint to retain image content. In the second module, we design a learning-based multibit zero-watermarking scheme where the trained invariant features are projected onto a set of trainable reference codes optimized to match a target binary message. Extensive experiments on diverse image datasets and a wide range of distortions s...